Abstract This paper presents a novel wavelet‐based transform and coding scheme for irregular meshes. The transform preserves geometric features at lower resolutions by adaptive vertex sampling and retriangulation, resulting in more accurate subsampling and better avoidance of smoothing and aliasing artefacts. By employing octree‐based coding techniques, the encoding of both connectivity and geometry information is decoupled from any mesh traversal order, and allows for exploiting the intra‐band statistical dependencies between wavelet coefficients. Improvements over the state of the art obtained by our approach are three‐fold: (1) improved rate–distortion performance over Wavemesh and IPR for both the Hausdorff and root mean square distances at low‐to‐mid‐range bitrates, most obvious when clear geometric features are present while remaining competitive for smooth, feature‐poor models; (2) improved rendering performance at any triangle budget, translating to a better quality for the same runtime memory footprint; (3) improved visual quality when applying similar limits to the bitrate or triangle budget, showing more pronounced improvements than rate–distortion curves.

In previous work, the authors developed a modular no-reference framework that compresses FASTA files by applying a predict-and-residue method, as used in video coding. We extended this framework with support for Context-Adaptive Binary Arithmetic Coding (CABAC), while at the same time preserving random access functionality and offering support for the full IUB/IUPAC nucleic acid codes alphabet.

Complex software appliances typically consist of multiple software processes running concurrently to exploit the available computational resources in the hardware. However, the computational complexity of these software processes is often variable and the processes can interfere with each other. This can be an issue for real-time applications with a fixed deadline like low delay video encoding. In the context of High Efficiency Video Coding (HEVC), a limited number of publications have focused on controlling the complexity of an HEVC video encoder. In this paper, we propose a technique to control complexity by deciding between 2Nx2N merge mode and full encoding, at different Coding Unit (CU) depths. Our results demonstrate fast convergence to a given complexity threshold after a maximum of 10 frames, and a limited loss in rate-distortion performance (on average 2.84% Bjontegaard delta rate for 60% complexity reduction).

Complex software appliances can be deployed on hardware with limited available computational resources. This computational boundary puts an additional constraint on software applications. This can be an issue for real-time applications with a fixed time constraint such as low delay video encoding. In the context of High Efficiency Video Coding (HEVC), a limited number of publications have focused on controlling the complexity of an HEVC video encoder. In this paper, a technique is proposed to control complexity by deciding between 2N×2N merge mode and full encoding, at different Coding Unit (CU) depths. The technique is demonstrated in two encoders. The results demonstrate fast convergence to a given complexity threshold, and a limited loss in rate-distortion performance (on average 2.84% Bjontegaard delta rate for 40% complexity reduction).

Reservoir Computing Network (RCN) is a special type of the single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCN resulted in an effective and noise-robust RCN-based model for speech recognition. The aim of this work is to extend that study to the field of image processing. In particular, we investigate the potential of RCNs in achieving a competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The conducted experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise.

The searchability of video content is often limited to the descriptions authors and/or annotators care to provide. The level of description can range from absolutely nothing to fine-grained annotations at the level of frames. Based on these annotations, certain parts of the video content are more searchable than others.

Nowadays, the Web has become one of the main sources of biodiversity information. An increasing number of biodiversity research institutions add new specimens and their related information to their biological collections and make this information available on the Web. However, mechanisms which are currently available provide insufficient provenance of biodiversity information. In this paper, we propose a new biodiversity provenance model extending the W3C PROV Data Model. Biodiversity data is mapped to terms from relevant ontologies, such as Dublin Core and GeoSPARQL, stored in triple stores and queried using SPARQL endpoints. Additionally, we provide a use case using our provenance model to enrich collection data.

In professional markets, there is a growing need to incorporate off-the-shelf consumer-level hardware to reduce costs and speed up development. Typically, these hardware components contain hardware-accelerated video encoders, which are limited to the more popular YUV4:2:0 profiles. For screen content coding (SCC) (e.g., desktop sharing, supervisory control and data acquisition applications, etc.), chroma subsampling can produce specific artifacts that are disturbing in high-quality professional applications. Current research on SCC focuses on developing specific screen content profiles and coding tools, which are not likely to be integrated in consumer electronics. In this paper, a method is explored to improve the chroma quality of SCC using YUV4:2:0 codecs. It is possible to recover parts of the chroma component based on the luma component and the subsampling filter that is used at the encoder device. A 1.63-dB peak signal-to-noise ratio-chroma improvement and 0.051 structural similarity-chroma improvement are measured on average for screen content sequences. The proposed method still provides improvements if compression is used.

High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.

In order to assess the trustworthiness of information on social media, a consumer needs to understand where this information comes from, and which processes were involved in its creation. The entities, agents and activities involved in the creation of a piece of information are referred to as its provenance, which was standardized by W3C PROV. However, current social media APIs cannot always capture the full lineage of every message, leaving the consumer with incomplete or missing provenance, which is crucial for judging the trust it carries. Therefore in this paper, we propose an approach to reconstruct the provenance of messages on social media on multiple levels. To obtain a fine-grained level of provenance, we use an approach from prior work to reconstruct information cascades with high certainty, and map them to PROV using the PROV-SAID extension for social media. To obtain a coarse-grained level of provenance, we adapt our similarity-based, fuzzy provenance reconstruction approach -- previously applied on news. We illustrate the power of the combination by providing the reconstructed provenance of a limited social media dataset gathered during the 2012 Olympics, for which we were able to reconstruct a significant amount of previously unidentified connections.

Interest in eLearning environments is increasing, as well as in digital textbooks and gamification. The advantages of gamification in the context of education have been proven. However, gamified educational material, such as gamified digital textbooks and systems, are scarce. As an answer to the need for such material, the framework GEL (Gamification for EPUB using Linked Data) has been developed. GEL allows to incorporate gamification concepts in a digital textbook, using EPUB 3 and Linked Data. As part of GEL, we created the ontology Gamification Ontology (GO), representing the different gamification concepts, and a JavaScript library. Using GO allows to discover other gamified books, to share gamification concepts between applications and to separate the processing and representation of the gamification concepts.

Communication using internet and digital media is more and more popular. Therefore, the security and privacy of data transmission are highly demanded. One effective technique providing this requirement is information hiding. This technique allows to conceal secret information into a video file, an audio, or a picture. In this paper, we propose a low complexity out-of-the-loop information hiding algorithm for a video pre-encoded with the high efficiency video coding standard. Only selected components such as the motion vector difference and transform coefficients of the video are extracted and modified, bypassing the need of fully decoding and re-encoding the video. In order to reduce the propagation error caused by hiding information, the dependency between video frames is taken into account when distributing the information over the frame. Several embedding strategies are investigated. The experimental results show that the information should be hidden in smaller blocks to reduce quality loss. Using a smart distribution of information across the frames can keep the quality loss under 1 dB PSNR for an information payload of 15 kbps. When such a strategy is used, embedding information in the transform coefficients only slightly outperforms the modification of motion vector differences.

Hyperspectral image (HSI) classification is one of the most widely used methods for scene analysis from hyperspectral imagery. In the past, many different engineered features have been proposed for the HSI classification problem. In this paper, however, we propose a feature learning approach for hyperspectral image classification based on convolutional neural networks (CNNs). The proposed CNN model is able to learn structured features, roughly resembling different spectral band-pass filters, directly from the hyperspectral input data. Our experimental results, conducted on a commonly-used remote sensing hyperspectral dataset, show that the proposed method provides classification results that are among the state-of-the-art, without using any prior knowledge or engineered features.

Content providers create different versions of a video to accommodate different end-user devices and network conditions. However, each of these versions requires a resource intensive encoding process. To reduce the computational complexity of the encodings, this paper proposes a fast simultaneous encoder. This encoder takes a single video as input and creates a number of bit streams encoded with different parameters. Only one version of the video is created with a full encode, whereas encoding of the other versions is accelerated by exploiting the correlation with the fully encoded version using machine learning techniques. In a practical scenario, the fast simultaneous encoder achieves a complexity reduction of 67.3% with a bit rate increase of 5.2% compared to performing a full encode of each version.

This paper introduces a novel approach for coding luminance images using kernel-based adaptive filtering and context-adaptive arithmetic coding. This approach tackles the problem that is present in current image and video coders; these coders depend on assumptions of the image and are constrained by the linearity of their predictors. The efficacy of the predictors determines the compression gain. The goal is to create a generic image coder that learns and adapts to the characteristics of the signals and handles nonlinearity in the prediction. Results show that pixel luminance prediction using the Kernel Least Mean Squares (KLMS) yields a significant gain compared to the standard Least Mean Squares algorithm. By coding the residual using a Context-Adaptive Arithmetic Coder (CAAC), the codec is able to outperform the current industry standards of lossless image coding. An average bitrate reduction of more than 2.5% is found for the used test set.

We have created and evaluated an algorithm capable of deduplicating and clustering exact- and near-duplicate media items of type photo and video that get shared on multiple social networks in the context of events. This algorithm works in an entirely ad hoc manner without requiring any pre-calculation. When people attend events, they more and more share event-related media items publicly on social networks to let their social network contacts relive and witness the attended events. In the past, we have worked on methods to accumulate such public user-generated multimedia content in order to summarize events visually, for example, in the form of media galleries or slideshows. In this paper, first, we introduce social-network-specific reasons and challenges that cause near-duplicate media items. Second, we detail an algorithm for the task of deduplicating and clustering exact- and near-duplicate media items stemming from multiple social networks. Finally, we evaluate the algorithm's strengths and weaknesses and thoroughly compare its performance with the state-of-the-art feature detection algorithms SIFT, ASIFT and SURF and show that for the given use case it performs almost equally well accuracy-wise, but strongly outperforms speed-wise.

The visualization of (ultra) high-resolution compositions created from multiple input bitstreams requires several decoders at the receiving device. Therefore, not all devices can properly display such compositions. To address this problem, the input streams are decoded, merged into a single video, and re-encoded by a transcoder in the network. However, this approach requires a computationally complex re-encoding step. To reduce this complexity, information from the input bit-streams can be reused during transcoding. In HEVC, simply reusing the original encoding information is not compression efficient if the inserted content is not aligned with the grid of coded blocks. In this paper, we applied feature selection based on a decision tree, which was used in a fast HEVC transcoding model for misaligned content. The performance varies depending on the shift and average transform size in the original sequence, resulting in complexity reductions of up to 76%.

Transrating is a useful tool for adapting the bitrate of a video stream. Reducing the complexity of transrating is extremely important, especially for high efficiency video coding (HEVC) based transrating where the encoder is very time consuming. TZSearch is currently the default integer motion estimation (ME) algorithm in the HEVC reference encoder thanks to its excellent performance in reducing the complexity of ME. However, this algorithm is characterized by a fixed search area and search pattern, which can be considered sub optimal for transrating. In this paper, we improve the performance of HEVC transrating by optimizing this TZSearch algorithm. Utilizing the correlation between input and output motion vectors, we propose a fast search scheme including three steps. First, the initial search point is selected. Then, using the rate-distortion cost of this starting point, the search size is determined according to an online-trained Bayes decision rule. Finally, two proposed search algorithms are described for refining the starting point. Experimental results show that our proposed TZSearch scheme can reduce the complexity of transrating while improving the coding performance in terms of bit rate saving.

Kernel regression has been proven successful for image de-noising, deblocking and reconstruction. These techniques lay the foundation for new image coding opportunities. In this paper, we introduce a novel compression scheme: Sparse Steering Kernel Synthesis Coding (SSKSC). This pre- and postprocessor for JPEG performs non-uniform sampling based on the smoothness of an image, and reconstructs the missing pixels using adaptive kernel regression. At the same time, the kernel regression reduces the blocking artifacts from the JPEG coding. Crucial to this technique is that non-uniform sampling is performed while maintaining only a small overhead for signalization. Compared to JPEG, SSKSC achieves a compression gain for low bits-per-pixel regions of 50% or more for PSNR and SSIM. A PSNR gain is typically in the 0.0-0.5 bpp range, and an SSIM gain can mostly be achieved in the 0.0-1.0 bpp range.

The capabilities of off-the-shelf consumer electronics are continuously increasing, making them interesting candidates to integrate into industrial visualization systems for reducing the cost of these systems or speed up development. However, in the context of video compression, hardware acceleration on consumer electronics is typically only provided for more common, consumer oriented YUV 4:2:0 profiles. Meanwhile, industrial applications often require YUV 4:4:4 formats due to synthetic visual data. Upscaling YUV 4:2:0 to YUV 4:4:4 using conventional filters results in visual artifacts, specifically for synthetic content. To improve the quality of the chroma components, this paper proposes to extend the well-known guided image filter through content-adaptive selection of the filter radius, and analyze different strategies for limiting complexity. This results in a 3.5 dB improvement of the chroma PSNR compared to conventional filters.

In an IPTV environment, different techniques exist to provide faster random access or equivalently, a faster channel switching experience, but none of them provide backward compatibility and limited overhead at the same time. In this paper, a technique is proposed to increase the random access frequency for the High Efficiency Video Coding (HEVC) standard by using a single-loop scalable version of it. This is achieved by encoding an enhancement layer with a higher frequency of random access points compared to the base layer. With this scalable configuration, a backward compatible base stream remains present. The proposed technique requires 12.4% less bitrate compared to sending the fast and slow switching streams in a non-scalable way. Compared to the almost standardized multi-loop scalable extension of HEVC, 5.5% less bitrate is needed on the core of the IPTV network and 12.5% less bitrate on the access network during steady state conditions. Moreover, the multi-loop scalable extension does not provide backward compatibility with legacy HEVC decoders.

The ability to automatically determine the road type from sensor data is of great significance for automatic annotation of routes and autonomous navigation of robots and vehicles. In this paper, we present a novel algorithm for content-based road type classification from images. The proposed method learns discriminative features from training data in an unsupervised manner, thus not requiring domain-specific feature engineering. This is an advantage over related road surface classification algorithms which are only able to make a distinction between pre-specified uniform terrains. In order to evaluate the proposed approach, we have constructed a challenging road image dataset of 20,000 samples from real-world road images in the paved and unpaved road classes. Experimental results on this dataset show that the proposed algorithm can achieve state-of-the-art performance in road type classification.

One of the drawbacks of current 3DTV systems are the glasses required to separate the 3D views. This problem can be solved by using autostereoscopic 3D displays, which generates a 3D scene from different viewpoints. These viewpoints are synthesized in the display based on depth information of the scene. However, currently no depth information is available for stereoscopic video. Therefore, depth estimation based on the stereoscopic input is required. This depth estimation should be low-complex and real-time for consumer electronic devices. Due to this low-complexity requirement, the quality of such depth maps will be limited. In this paper, we propose a guided depth filter, which filters the estimated depth map by using the downsampled chroma signal of the corresponding texture view as a guide. This allows to refine the borders of objects which reduces visible inpainting artifacts caused by the view synthesis. The proposed algorithm has been evaluated by expert viewing to show that it is independent of the depth map estimation and view synthesis of the autostereoscopic display.

Wyner-Ziv video coding constitutes an alluring paradigm for visual sensor networks, offering efficient video compression with low complexity encoding characteristics. This work presents a novel hash-driven Wyner-Ziv video coding architecture for visual sensors, implementing the principles of successively refined Wyner-Ziv coding. To this end, so-called side-information refinement levels are constructed for a number of grouped frequency bands of the discrete cosine transform. The proposed codec creates side-information by means of an original overlapped block motion estimation and pixel-based multihypothesis prediction technique, specifically built around the pursued refinement strategy. The quality of the side-information generated at every refinement level is successively improved, leading to gradually enhanced Wyner-Ziv coding performance. Additionally, this work explores several temporal prediction structures, including a new hierarchical unidirectional prediction structure, providing both temporal scalability and low delay coding. Experimental results include a thorough evaluation of our novel Wyner-Ziv codec, assessing the impact of the proposed successive refinement scheme and the supported temporal prediction structures for a wide range of hash configurations and group of pictures sizes. The results report significant compression gains with respect to benchmark systems in Wyner-Ziv video coding (e.g., up to 42.03% over DISCOVER) as well as versus alternative state-of-the-art schemes refining the side-information.

Despite adding emotions to applications has proven to enhance the user experience, emotion recognition applications are still not widely available nor used. Within this paper, emotion recognition is done on Twitter tweets using six emotion classification algorithms that are compared on precision and timing. The paper shows that precision can be enhanced by 5.02% compared to the current state-of-the-art by improving the features. Furthermore, the presented algorithms work in real-time.

The H.264/AVC video coding standard introduces some improved tools in order to increase compression efficiency. One of these new features is the variable block-size motion estimation. Moreover, H.264/AVC defines different prediction structures which include bi-directional predictions and, more recently, the hierarchical one. These structures also includes new Group Of Picture patterns which outperform the traditional ones. In the literature, several techniques have been proposed over the last few years which are aimed at accelerating the traditional inter prediction process, but there are no many works focusing on bidirectional and hierarchical predictions. In this paper, with the emergence of manycore processors or accelerators, a step forward is taken towards an implementation of an H.264/AVC inter prediction algorithm on a Graphics Processing Unit. The results show a negligible rate distortion drop with a time reduction of up to 92% for the complete H.264/AVC encoder.

To incorporate the newly developed High Efficiency Video Coding (HEVC) standard in real-life network applications, efficient transrating algorithms are required. We propose a fast transrating scheme, based on the early prediction of the partition split-flags in P pictures. Using machine learning techniques, the correlation between co-located partitions at different quantizations is investigated. This results in a model which predicts the split-flag and gives the associated prediction accuracy so that the splitting process in the transcoder is optimized. At each partition depth, the model indicates whether the full rate-distortion cost evaluations should be performed at the current depth, or if the partition can be split immediately. Experimental results show that the proposed transcoder reduces the complexity of the transrating process by 76.04%, while maintaining the coding efficiency of a cascaded decoder-encoder.

When middlebox devices should be able to adapt an encrypted video stream in the network without having the decryption key, format-compliant partial encryption schemes should be applied. In this paper, we propose such encryption schemes for the recently standardized High Efficiency Video Coding (HEVC) standard. By encrypting specific syntax elements like the sign of the residual information, the sign of the motion vector (MV) difference, the MV prediction index, and the MV reference index, format compliance and the possibility for adaptation are offered. Scrambling performance gradually increases when shifting from encrypting the motion information to encrypting the residual sign and finally to the combination thereof. Applying all these techniques has a negligible impact on the compression efficiency.

Searching for relevant webpages and following hyperlinks to related content is a widely accepted and effective approach to information seeking on the textual web. Existing work on multimedia information retrieval has focused on search for individual relevant items or on content linking without specific attention to search results. We describe our research exploring integrated multimodal search and hyperlinking for multimedia data. Our investigation is based on the MediaEval 2012 Search and Hyperlinking task. This includes a known-item search task using the Blip10000 internet video collection, where automatically created hyperlinks link each relevant item to related items within the collection. The search test queries and link assessment for this task was generated using the Amazon Mechanical Turk crowdsourcing platform. Our investigation examines a range of alternative methods which seek to address the challenges of search and hyperlinking using multimodal approaches. The results of our experiments are used to propose a research agenda for developing effective techniques for search and hyperlinking of multimedia content.

Broadcasters produce vast collections of video content. However, the lack of fine-grained annotations makes it difficult to retrieve video fragments of interest from these vast collections. Indeed, manual annotation of video content is labour-intensive and time-consuming. Moreover, the applicability of algorithms for automatic annotation of video content is limited, given that too many prerequisites need to be fulfilled and that a lot of concepts are unidentifiable. At the same time, people are using social media to share their thoughts about the content they view on television. Therefore, in this Ph.D. research, we plan to investigate novel machine learning-based approaches towards the task of fine-grained annotation of broadcast video content, fusing the collective knowledge present in social media with the output of audio-visual content analysis algorithms.

H.264/AVC applies an adaptive in-loop deblocking filter in order to remove blocking artifacts. The deblocking filter is adaptively controlled by using the boundary strength (BS) parameter. Although the filter achieves advantages in reducing blocking artifacts, there are two problems we should consider to improve the filtering performance. The first problem is that the filter applies just a strong and the strongest filter to filter intra coded blocks. The strong filter can remove blocking artifacts at smooth regions; however, it removes detail or sharpness of pictures at complex regions. The second problem is that H.264/AVC does not pay attention in removing corner outliers which appear when an edge of an object crosses a corner of a block. Therefore, corner outliers clearly appear and reduce the subjective quality of reconstructed pictures. In this paper, we propose an adaptive deblocking filter scheme which removes corner outliers, and which utilizes the intra prediction information of intra coded blocks to adaptively select the BS. Experimental results show that our proposed filter significantly enhances the subjective quality by removing blocking artifacts at smooth regions while maintaining details or sharpness at complex regions. In addition, the objective quality is improved, resulting in PSNR gains of up to 0.16 dB.

In this paper, we describe a novel approach for the automatic generation of a GEO-MASHUP related to a user his outdoor activities. Each mashup consists of online geotagged media resources related to the geographic keypoints where the outdoor activity was performed. In order to detect candidate keypoints, we search low-activity locations based on the travelling distance over time. Subsequently, we filter out route-specific keypoints (such as traffic lights) using online trajectory information. Finally, the remaining keypoints are fed to a set of social media web services to retrieve the geotagged media which summarizes the user's activity. The GEO-MASHUP demonstrator, which is evaluated in real-world conditions, shows the feasibility of our novel approach.

Distributed video coding (DVC) is a coding paradigm that entails low complexity encoding by exploiting the source statistics at the decoder. To improve the DVC coding efficiency, this paper proposes a novel adaptive technique for mode decision to control and take advantage of skip mode and intra mode in DVC. The adaptive mode decision is not only based on quality of key frames but also the rate of Wyner-Ziv (WZ) frames. To improve noise distribution estimation for a more accurate mode decision, a residual motion compensation is proposed to estimate a current noise residue based on a previously decoded frame. The experimental results show that the proposed adaptive mode decision DVC significantly improves the rate distortion performance without increasing the encoding complexity. For a GOP size of 2 on the set of test sequences, the average bitrate saving of the proposed codec is 35.5% on WZ frames compared with the DISCOVER codec.

The concept of Linked Data has made its entrance in the cultural heritage sector due to its potential use for the integration of heterogeneous collections and deriving additional value out of existing metadata. However, practitioners and researchers alike need a better understanding of what outcome they can reasonably expect of the reconciliation process between their local metadata and established controlled vocabularies which are already a part of the Linked Data cloud. This paper offers an in‐depth analysis of how a locally developed vocabulary can be successfully reconciled with the Library of Congress Subject Headings (LCSH) and the Arts and Architecture Thesaurus (AAT) through the help of a general‐purpose tool for interactive data transformation (OpenRefine). Issues negatively affecting the reconciliation process are identified and solutions are proposed in order to derive maximum value from existing metadata and controlled vocabularies in an automated manner.

When a video delivery system provides different representations of the same sequence, solutions can be found in simulcast coding, scalable coding, and transcoding. The major downside of transcoding is the additional complexity needed to re-encode the video sequence in its new form. When one fidelity version of the video stream should be transmitted over the network, scalable coding is less efficient compared to single layer coding because of the layering overhead. Finally, simulcast encoding results in large storage requirements. In this paper, we propose an intermediate solution providing transcoding at a low complexity by the aid of control streams. We define a control stream as a regular video stream from which residual information is removed. With these control streams, the complexity of the encoding step in the transcoder can be reduced to decoder complexity. As a result of the removal of residual information, these control streams take up 62.8% less bitrate compared to simulcast coding for a test set based on High Efficiency Video Coding (HEVC). Additionally, when compared to scalable coding, an efficient single layer video stream can be provided without the 16.6% bitrate increase caused by transmitting a layered bitstream.

Deblocking filtering in the H.264/AVC standard is a computationally complex process because of the filter's high content adaptivity. Furthermore, the deblocking filter introduces a significant number of data dependencies, making parallel processing not obvious. Our previous works analyzed the dependencies of the filter and proposed a massively-parallel implementation, specifically tailored for execution on a single GPU. In this paper, we extend this work by proposing a parallel processing scheme for accelerating deblocking filtering using multiple CPU cores or GPUs. This scheme allows for standard-compliant filtering, regardless of slice configuration. Results show that our multi-GPU implementation using our proposed scheme achieves faster-than real-time deblocking at over 3794 frames per second for 1080p video pictures by using three GPUs. A multi-core CPU implementation using 8 CPU cores allows 1080p deblocking filtering of up to 695 frames per second.

Distributed video coding (DVC) systems described in the literature often make use of a feedback channel to determine the rate. However, supporting such a feedback channel in practice may be difficult, particularly considering that current approaches are unable to incorporate constraints on feedback channel usage. Therefore, in this paper we propose a system in which the number of requests per Wyner-Ziv frame can be constrained to a fixed value. Our technique involves decoder-side rate estimation, modeling of its accuracy, and finally defining the number of Wyner-Ziv bits for each of the requests allowed. The experimental results indicate that the performance loss is limited when compared to a configuration operating without constraints on the feedback channel.

Scalable video coding performance largely depends on the underlying single layer coding efficiency. In this paper, the quality scalability capabilities are evaluated on a base of the new High Efficiency Video Coding (HEVC) standard under development. To enable the evaluation, a multi-loop codec has been designed using HEVC. Adaptive inter-layer prediction is realized by including the lower layer in the reference list of the enhancement layer. As a result, adaptive scalability on frame level and on prediction unit level is accomplished. Compared to single layer coding, 19.4% Bjontegaard Delta bitrate increase is measured over approximately a 30dB to 40dB PSNR range. When compared to simulcast, 20.6% bitrate reduction can be achieved. Under equivalent conditions, the presented technique achieves 43.8% bitrate reduction over Coarse Grain Scalability of the SVC - H.264/AVC-based standard.

Distributed video coding (DVC) is a video coding paradigm that allows for a low-complexity encoding process by exploiting the temporal redundancies in a video sequence at the decoder side. State-of-the-art DVC systems exhibit a structural coding delay since exploiting the temporal redundancies through motion-compensated interpolation requires the frames to be decoded out of order. To alleviate this problem, we propose a system based on motion-compensated extrapolation that allows for efficient low-delay video coding with low complexity at the encoder. The proposed extrapolation technique first estimates the motion field between the two most recently decoded frames using the Lucas–Kanade algorithm. The obtained motion field is then extrapolated to the current frame using an extrapolation grid. The proposed techniques are implemented into a novel architecture featuring hybrid block-frequency Wyner–Ziv coding as well as mode decision. Results show that having references from both temporal directions in interpolation provides superior rate-distortion performance over a single temporal direction in extrapolation, as expected. However, the proposed extrapolation method is particularly suitable for low-delay coding as it performs better than H.264/AVC intra, and it is even able to outperform the interpolation-based DVC codec from DISCOVER for several sequences.

Many of the distributed video coding (DVC) systems described in the literature make use of a feedback channel from the decoder to the encoder to determine the rate. However, the number of requests through the feedback channel is often high, and as a result the overall delay of the system could be unacceptable in practical applications. As a solution, feedback-free DVC systems have been proposed, but the problem with these solutions is that they incorporate a difficult trade-off between encoder complexity and compression performance. Recognizing that a limited form of feedback may be supported in many video-streaming scenarios, in this paper we propose a method for constraining the number of feedback requests to a fixed maximum number of$N$requests for an entire Wyner-Ziv (WZ) frame. The proposed technique estimates the WZ rate at the decoder using information obtained from previously decoded WZ frames and defines the$N$requests by minimizing the expected rate overhead. Tests on eight sequences show that the rate penalty is less than 5% when only five requests are allowed per WZ frame (for a group of pictures of size four). Furthermore, due to improvements from previous work, the system is able to perform better than or similar to DISCOVER even when up to two requests per WZ frame are allowed. The practical usefulness of the proposed approach is studied by estimating end-to-end delay and encoder buffer requirements, indicating that DVC with constrained feedback can be an important solution in the context of video-streaming scenarios.

There's a gap in consumer communications on how networks, devices, and services handle and identify space & time in media resources in the Web. W3C's Media Fragments URI specification, which we edited and contributed to over the last couple of years, not only bridges this gap by homogeneously opening up time-related media to the Internet crowd, but also makes time-related annotation feasible for hyperlinking into that media, thus finally also providing the necessary support for this already omnipresent `third dimension' time into the Internet. As such, this uniform approach using the standardised (semantic) web technology stack will improve search, discovery, usage, management, security, and linking of related media resources and its fragments.

In video coding systems using adaptive arithmetic coding to compress texture information, the employed symbol probability models need to be retrained every time the coding process moves into an area with different texture. To avoid this inefficiency, we propose to replace the probability models used in the original coder with multiple switchable sets of probability models. We determine the model set to use in each spatial region in an optimal manner, taking into account the additional signaling overhead. Experimental results show that this approach, when applied to H.264/AVC's context-based adaptive binary arithmetic coder (CABAC), yields significant bit-rate savings, which are comparable to or higher than those obtained using alternative improvements to CABAC previously proposed in the literature.

Triggered by the challenging prerequisites of wireless capsule endoscopic video technology, this paper presents a novel distributed video coding (DVC) scheme, which employs an original hash-based side-information creation method at the decoder. In contrast to existing DVC schemes, the proposed codec generates high quality side-information at the decoder, even under the strenuous motion conditions encountered in endoscopic video. Performance evaluation using broad endoscopic video material shows that the proposed approach brings notable and consistent compression gains over various state-of-the-art video codecs at the additional benefit of vastly reduced encoding complexity.

Scalable video coding (SVC) allows encoded bitstreams to be adapted. However, most bitstreams do not incorporate this scalability so bitstreams have to be adapted multiple times to accommodate for varying network conditions or end-user devices. Each adaptation incorporates an additional loss of quality due to transcoding. To overcome this issue, we propose a single transcoding step from H.264/AVC to SVC. Doing so, the resulting bitstream can be freely adapted without any additional quality reduction. Open-loop transcoding architectures can be used for H.264/AVC-to-SVC transcoding with a low complexity, although these architectures suffer from drift artifacts. Closed-loop transcoding, on the other hand, requires a higher complexity. To overcome the drawbacks of both systems, we propose combining both techniques.

Many applications require real-time decoding of high-resolution video pictures, for example, quick editing of video sequences in video editing applications. To increase decoding speed, parallelism can be exploited, yet, block-based image and video coding standards are difficult to decode in parallel because of the high number of dependencies between blocks. This paper investigates the parallel decoding capabilities of the new JPEG XR image coding standard for use on the massively-parallel architecture of the GPU. The potential of parallelism of the hierarchical frequency coding scheme used in the standard is addressed and a parallel decoding scheme is described suitable for real-time decoding of Ultra High Definition (4320p) Motion JPEG XR video sequences. Our results show a decoding speed of up to 46 frames per second for Ultra High Definition (4320p) sequences with high-dynamic range (32-bit/4:2:0) luma and chroma components.

During the past decade, Distributed Video Coding (DVC) has emerged as a new video coding paradigm, shifting the complexity from the encoder - to the decoder-side. This paper addresses a problem of current DVC architectures that has not been studied in the literature so far, that is, the mismatch between the intra and Wyner-Ziv (WZ) quantization processes. Due to this mismatch, WZ rate is spent even for spatial regions that are accurately approximated by the side-information. As a solution, this paper proposes side-information generation using selective unidirectional motion compensation from temporally adjacent WZ frames. Experimental results show that the proposed approach yields promising WZ rate gains of up to 7% relative to the conventional method.

HTTP adaptive streaming was introduced with the general idea that user agents interpret a manifest file (describing different representations and segments of the media); where-after they retrieve the media content using sequential HTTP progressive download operations. MPEG started with the standardization of an HTTP streaming protocol, defining the structure and semantics of a manifest file and additional restrictions and extensions for container formats. At the same time, W3C is working on a specification for addressing media fragments on the Web using Uniform Resource Identifiers. The latter not only defines the URI syntax for media fragment identifiers but also the protocol for retrieving media fragments over HTTP. In this paper, we elaborate on the role of Media Fragment URIs within HTTP adaptive streaming scenarios. First, we elaborate on how different media representations can be addressed by means of Media Fragment URIs, by using track fragments. Additionally, we illustrate how HTTP adaptive streaming is realized relying on the Media Fragments URI retrieval protocol. To validate the presented ideas, we implemented Apple's HTTP Live streaming technique using Media Fragment URIs.

This paper proposes a novel multi-sensor fire detection method based on ordinary video images and the amplitude images of a time-of-flight camera. Using this multi-modal in formation, flame regions can be detected very accurately. Regions with high accumulative amplitude differences and high values in all detail images of the amplitude image its discrete wavelet transform, are labeled as candidate flame regions. Simultaneously, moving objects in the visual images are also investigated. Objects which possess the experimentally found low-cost flame features are also labeled as candidate flame region. Finally, if one of the visual and amplitude candidate flame regions overlap, fire alarm is given. Experiments show that the proposed detector has an average flame detection rate of 92% with no false positive detections.

In the current development of HEVC, compression performance improved significantly compared to H.264/AVC for both inter pictures and intra pictures. With intra compression, the main reason for this improvement is the large in crease in intra prediction directions (up to 34). The downside of having a larger number of modes is that they increase the signaling overhead in the bitstream. In this paper, a low complexity intra mode prediction algorithm is proposed which improves the mode prediction accuracy. This is achieved by exploiting the correlation between the prediction directions of the neighboring prediction units and that of the encoded prediction unit. As a result, more efficient intra mode signaling can be achieved with minimal impact on encoder and decoder complexity. On average, 0.33% bitrate improvement is obtained by employing the proposed algorithm. For sequences that are encoded with a high number of directional intra modes, around 1% bitrate improvement is measured.

The deblocking filter in the MPEG-4 AVC/H.264 standard is computationally complex because of its high content adaptivity, resulting in a significant number of data dependencies. These data dependencies interfere with parallel filtering of multiple macroblocks (MBs) on massively parallel architectures. In this letter, we introduce a novel MB partitioning scheme for concurrent deblocking in the MPEG-4 AVC/H.264 standard, based on our idea of deblocking filter independency, a corrected version of the limited error propagation effect proposed in the letter. Our proposed scheme enables concurrent MB deblocking of luma samples with limited synchronization effort, independently of slice configuration, and is compliant with the MPEG-4 H.264/AVC standard. We implemented the method on the massively parallel architecture of the graphics processing unit (GPU). Experimental results show that our GPU implementation achieves faster-than real-time deblocking at 1309 frames per second for 1080p video pictures. Both software-based deblocking filters and state-of-the-art GPU-enabled algorithms are outperformed in terms of speed by factors up to 10.2 and 19.5, respectively, for 1080p video pictures.

The automatic detection of meaningful phases in a soccer game depends on the accurate localization of players and the ball at each moment. However, the automatic analysis of soccer sequences is a challenging task due to the presence of fast moving multiple objects. For this purpose, we present a multi-camera analysis system that yields the position of the ball and players on a common ground plane. The detection in each camera is based on a code-book algorithm and different features are used to classify the detected blobs. The detection results of each camera are transformed using homography to a virtual top-view of the playing field. Within this virtual top-view we merge trajectory information of the different cameras allowing to refine the found positions. In this paper, we evaluate the system on a public SOCCER dataset and end with a discussion of possible improvements of the dataset.

While distributed video coding (DVC) has emerged as a new video coding paradigm, the compression performance of current systems is still low compared to conventional solutions such as H.264/AVC. While the latter uses many coding modes and an efficient mode decision strategy for choosing the best mode, in DVC, only a limited number of modes has been developed so far. Since encoder-side mode decision in DVC increases encoder's complexity, in this paper, we introduce decoder-side mode decision choosing between bitplane WZ coding and bitplane intra coding. This strategy proves to be efficient, delivering rate gains up to 22% over DISCOVER, without increasing the complexity at the encoder.

In this demonstration, we present NinSuna, a fully integrated multimedia content adaptation platform based on Semantic Web technologies. Moreover, we show how NinSuna can be used as a server-side implementation of W3C Media Fragment URIs, which enables addressing media fragments on the Web using URIs.

A number of different approaches exist to deliver multimedia content to the end-user through the Web. Within classic client-server architectures, three main media delivery methods exist: HTTP download, real-time streaming, and HTTP streaming. The latter combines the advantages of both the HTTP download and the traditional streaming approach. In this paper, we provide an overview and analysis of two existing technologies which are based on HTTP streaming: Smooth Streaming and HTTP Live Streaming. Further, we propose an enhancement for HTTP Streaming, called BSD-based HTTP streaming. This enhanced media delivery technique works on an ordinary HTTP Web server, makes use of existing metadata specifications, and does not apply any restriction regarding the media formats used and the way media resources are organized on the server.

Scalable Video Coding (SVC) is a recent extension of the ISO/ITU Advanced Video Coding (H.264/AVC) standard, which allows adapting the bitstream easily by dropping some parts of it named as layers. This adaptation makes possible that the same bitstream meets the requirements for reliable delivery of video to diverse clients over heterogeneous networks using temporal, spatial or SNR scalability, combined or separately. Since the SVC design requires scalability to be provided at the encoder side, the existing contents cannot benefit of it. Efficient techniques for converting contents without scalability to a scalable format are desirable. In this paper, an approach for temporal scalability transcoding from H.264/AVC to SVC is presented and the impact of the GOP size is analyzed. Independently of the GOP size chosen, around a 60% of time saving is achieved while maintaining the coding efficiency. Moreover, this technique could be used to transform an H.264/AVC bitstream without temporal scalability to another H.264/AVC bitstream with hierarchical structures that provides temporal scalability.

Aiming for low-complexity encoding, distributed video coders still fail to achieve the performance of current industrial standards for video coding. One of most important problems in this area is the accurate modeling of the correlation between the predicted signal and the original video. In our previous work we showed that exploiting the quantization distortion can significantly improve the accuracy of a correlation estimator. In this paper we describe how the quantization distortion can be exploited purely at the decoder side without any performance penalty when compared to an encoder-aided system. As a result, the proposed correlation estimator delivers state-of-the-art modeling accuracy while neatly fitting the low-encoder-complexity characteristic of distributed video coding.

In this paper, we describe two examples of implementations of the Media Fragments URI specification which is currently being developed by the W3C Media Fragments Working Group. The group's mission is to create standard addressing schemes for media fragments on the Web using Uniform Resource Identifiers (URIs). We describe two scenarios to illustrate the implementations. More specifically, we show how User Agents (UA) will either be able to resolve media fragment URIs without help from the server, or will make use of a media fragments-aware server. Finally, we present some ongoing discussions and issues regarding the implementation of the Media Fragments specification.

This paper presents a multi-view homography-based approach for object localization in H.264/AVC compressed video surveillance sequences. The proposed novel, low-complexity method is able to accurately localize moving objects on a ground plane using multiple camera data. Contrary to existing work that exploits motion vectors for object detection and tracking, our compressed domain multi-view object localization solely uses macroblock (MB) partition information. Foreground segmentation is performed on single view compressed video data using MB partition-based temporal differencing. Blob merging, convex hull fitting and noise removal are applied on the resulting foreground views to extract objects. Once relevant objects are found in single views, they are projected onto a ground plane by exploiting the homography constraint. Since projected foreground MB views of multiple cameras will only overlap on points where foreground intersects the ground plane, object locations can be extracted by detecting local maxima on the accumulated ground plane image.

The health care sector is no longer imaginable without electronic health records. However, since the original idea of electronic health records was focused on data storage and not on data processing, a lot of current implementations do not take full advantage of the opportunities provided by computerization. This paper introduces the patient summary ontology for the representation of electronic health records and demonstrates the possibility to create next generation assisting clinical applications based on these semantic-aware electronic health records. Also, an architecture to interoperate with electronic health records formatted using other standards is presented.

This paper introduces the Movie Script Markup Language (MSML), a document specification for the structural representation of screenplay narratives for television and feature film drama production. Its definition was motivated by a lack of available structured and open formats that describe dramatic narrative but also support IT-based production methods of audiovisual drama. The MSML specification fully supports contemporary screenplay templates in a structured fashion, and adds provisions for drama manufacturing methods that allow drama crew to define how narrative can be translated to audiovisual material. A timing model based on timed petri nets is included to enable fine-grained event synchronization. Finally, MSML comprises an animation module through which narrative events can drive production elements like 3-D previsualization, content repurposing or studio automation. MSML is currently serialized into XML documents and is formally described by a complement of an XML Schema and ISO Schematron schema. The specification has been developed in close collaboration with actual drama production crew and has been implemented in a number of proof-of-concept demonstrators.

In this paper, we present motion-refined transcoding of H.264/AVC streams to SVC in the transform domain. By accurately taking into account both rate and distortion in the different layers on the one hand, and the SVC inter-layer motion prediction mechanisms on the other hand, the proposed transcoding architecture is able to improve rate-distortion performance over existing approaches. We propose a multilayer control mechanism that trades off performance between the different layers, resulting in 0.5 dB gains in the output SVC base layer.

This paper presents a new algorithm for moving object detection in the H.264/AVC compressed domain which relies on motion vector information. In contrast to other motion vector-based algorithms, special attention is paid to noisy motion vectors as they highly decrease the performance of these algorithms. We propose to estimate the reliability of motion vectors by comparing them with projected motion vectors from surrounding frames. As such, noisy motion vectors are localized. By combining this information with the magnitude of motion vectors, foreground objects are distinguished. Experimental results demonstrate that our algorithm achieves significantly better segmentation quality compared to other motion vector-based approaches.

A multitude of platforms with widely varying capabilities is available today for the consumption of broadcasted audiovisual content. Each class of media consumption devices should receive a product not only adapted to its capabilities, but also in a manner that fully conveys the cinematic narrative and hence provides true universal media experiences. This paper presents semantic adaptation processes that integrate with other drama media manufacturing processes and allow directors and other creative staff to naturally carry over cinematic decision making into the content adaptation domain, thereby using narrative story elements already available from other production processes. We discuss how the adaptation process interacts with other processes and what parameters influence the adaptations applied to audiovisual material, followed by an evaluation of our current implementation.

An IPTV network contains characteristics of both a unicast environment and a broadcast environment. For both environments, techniques to accelerate channel switching exist. In a unicast environment a bandwidth efficient implementation to accelerate channel switching can be obtained with non scalable video coding, while for a broadcast environment the same acceleration is more optimal using scalable video coding. For a combination of both environments, as is the case for an IPTV environment, none of the solutions is optimal. In this paper, we propose a compromise between scalable and non scalable video compression to adapt to the properties of an IPTV environment. One of the proposed configurations obtains bandwidth reductions on the access network of the IPTV network between 2.4% and 4.3% with a small increase between 0.1% and 1.6% on the core network of the IPTV network.

Today, users often want to consume personalized versions of multimedia content (e.g., a selection of the most interesting video scenes). These user preferences can be fulfilled by performing semantic adaptations, which are typically guided by semantic metadata. In this paper, we discuss how high-level semantic adaptations along the temporal axis can be performed using format-independent adaptation engines. Further, the impact of semantic adaptations applied to synchronized multimedia streams (e.g., synchronized audio and video streams) is investigated. H.264/AVC and AAC are used to evaluate our proposed approach, which relies on a format-independent adaptation framework based on MPEG-B BSDL and STX.

Requantization transcoding is a method for reducing the bit rate of compressed video bitstreams. Most research on requantization is concerned with the architectural design, the selection of a suitable quantizer, or the reduction of requantization errors. In this paper, we propose to incorporate a new dimension in requantization transcoding: the quantization offset. We compare two requantization methods: increasing the quantization step size and decreasing the quantization offset. Furthermore, we propose a novel heuristic for requantization transcoding based on a theoretical rate-distortion analysis. The experimental results for H.264/AVC video show that requantization is improved in rate-distortion sense with gains up to 1 dB for open-loop requantization of B pictures compared to requantization with fixed quantization offset.

Distributed video coding (DVC) targets video coding applications with low encoding complexity by generating a prediction of the video signal at the decoder. One of the most common architectures uses turbo codes to correct errors in this prediction. Unfortunately, a rigorous analysis of turbo coding in the context of DVC is missing. We have targeted one particular aspect of turbo coding: the stopping criterion. The stopping criterion indicates whether decoding was successful, i.e., whether the errors in the prediction signal have been corrected. In this paper we describe and compare several stopping criterions known from the field of channel coding and criterions currently used in DVC. As our results suggest the choice of the stopping criterion has a significant impact on the overall video-coding performance. Moreover, we have found that there are even better performing criterions than those currently used in DVC.

In Distributed Video Coding (DVC), compression is achieved by exploiting correlation between frames at the decoder, instead of at the encoder. More specifically, the decoder uses already decoded frames to generate side information Y for each Wyner-Ziv frame X, and corrects errors in Y using error correcting bits received from the encoder. For efficient use of these bits, the decoder needs information about the correlation between X available at the encoder and Y at the decoder. While several techniques for online estimation of correlation noise X - Y have been proposed, the quantization noise in Y has not been taken into account. As a solution, in this paper, we calculate the quantization noise of intra frames at the encoder and use this information at the decoder to improve the accuracy of the correlation noise estimation. Results indicate averageWyner-Ziv bit rate reductions up to 19.5% (Bjøntegaard delta) for coarse quantization.

When combining non-rectangular (shape-adaptive) partitioning of inter pictures with a rectangular block transform, some of the transforms will be applied to a residual signal which originates from two different predictions. This is a condition that is never encountered in any of the existing video coding standards, and the impact of this on the performance of the transform coder has not been investigated. In this paper we investigate the effect of these mixed-signal blocks on the coding gain for various transforms, and we compare these against the optimal KLT gain. We find that, despite the transformed residual block being composed of two parts that were predicted from different areas of the reference picture, correlation within mixed blocks is very similar to that of normal blocks. The DCT is only marginally suboptimal w.r.t. KLT. KLT has practical issues that will reduce its coding gain or increase the signaling overhead: transform bases need to be quantized and transmitted, or a number of fixed bases needs to be chosen offline. Therefore we recommend DCT be used for all types of blocks.

The scalable extension of H.264/AVC (SVC) was recently standardized, and offers scalability at a minor penalty in rate-distortion efficiency when compared to single-layer H.264/AVC coding. In SVC, a scaled version of the original video sequence can easily be extracted by dropping layers from the stream. However, most of the video content nowadays is still produced in a single-layer format. While decoding and reencoding is a possible solution to introduce scalability in the existing bitstreams, this is an approach which requires a tremendous amount of time and effort. In this paper, we show that transcoding can be used to intelligently derive scalable bitstreams from existing single-layer streams. We focus on SNR scalability, and introduce techniques that are able to create multiple quality layers in the bitstreams. We also discuss bitstream rewriting from SVC to H.264/AVC, and examine how our newly proposed architectures can benefit from the changes that were introduced for bitstream rewriting. Architectures with different rate distribution flexibility and computational complexity are discussed. Rate-distortion performance of transcoding is shown to be comparable to that of reencoding at a fraction of the time needed for the latter.

Current trends in smart homes suggest that several multimedia services will soon converge towards common standards and platforms. However this rapid evolution gives rise to several issues related to the management of a large number of multimedia streams in the home communication infrastructure. An issue of particular relevance is how a context acquisition system can be used to support the management of such a large number of streams with respect to the Quality of Service (QoS), to their adaptation to the available bandwidth or to the capacity of the involved devices, and to their migration and adaptation driven by the users’ needs that are implicitly or explicitly notified to the system. Under this scenario this paper describes the experience of the INTERMEDIA project in the exploitation of context information to support QoS, migration, and adaptation of multimedia streams.

In previous work, we introduced an H.264/AVC-to-SVC transcoder for creating SVC streams with multiple quality layers from a single-layer H.264/AVC stream. This architecture was able to restrain the error drift due to requantization of the residual coefficients. In this paper, we show that it is possible to further reduce the complexity, and completely eliminate the drift in the enhancement layer, by making use of the bitstream rewriting functionality in SVC. We propose different novel architectures, which are able to flexibly distribute the data among the different created layers.

In this paper, we present a spatial resolution reduction transcoding architecture for H.264/AVC, which extends open-loop transcoding with a low-complexity compensation technique in the reduced-resolution domain. The proposed architecture removes visual artifacts from the transcoded sequence, while keeping complexity significantly lower than more traditional cascaded decoder-encoder architectures. The refinement step of the proposed architecture can be used to further improve rate-distortion performance, at the cost of additional complexity. In this way, a dynamic-complexity transcoder is rendered possible.

In this paper, dynamic rate shaping for H.264/AVC intra-coded video streams is investigated. An analysis of the distortion distinguishes different error components that lead to the degradation of the output video stream. Experimental results show that the propagation error due to intra prediction has a major impact on the visual quality. This results from the highly increased number of dependencies which are invoked by the H.264/AVC intra prediction. In order to eliminate the propagation error, we propose to use single-loop compensation techniques. Both objective and subjective results show that the compensation techniques highly improve the visual quality.

Multimedia content adaptation is gaining importance because of the growing amount of multimedia content on the one hand and the growing diversity in usage environments on the other hand. Furthermore, to deal with the growing amount of coding formats for multimedia content, format-independent adaptation systems are desired. These systems support the exploitation of scalability to meet the usage environment, as well as semantic adaptations to meet the user preferences. In this demonstration, we present NinSuna, a fully integrated multimedia content adaptation platform based on semantic web technologies. It aims at being deployable in streaming environments and relies on format-independent semantic-aware adaptation engines.

We present a family of new macroblock partitions for H.264/AVC inter prediction. These modes allow a macroblock to be bipartitioned along a horizontal, vertical, or diagonal edge with an arbitrary location within the block. These new partitioning modes allow an encoder to adapt more closely to the local motion characteristics and improve its rate-distortion performance. In order to quantify the improvements we implemented the proposed partitioning modes in the context of the H.264/AVC video coding standard. Over half the coding efficiency of existing more general schemes is attained at a fraction of the number of added partitioning modes, and hence of encoder complexity, of those schemes. The results are consistent across different profiles, indicating that the proposed extension has good compatibility with the existing H.264/AVC tools for motion compensation and residual coding.

Due to the semantic gap, describing high-level semantic concepts with low-level visual features is a very challenging task. The classification of textures in scene images is intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired features for texture segmentation and an unsupervised method to link those texture features with semantic concepts. The calculation of the features is inspired by the human visual system and corresponds to cell outputs in the first stage of the visual cortex. Analogously to the processing principles of the cortex, self-organizing maps are employed for classification. The performance of the texture segmentation and labeling is evaluated on textures from the Brodatz album and on a real-life scenery image dataset. For both methods, a high percentage of pixels is correctly classified.

To attain efficient coding of sequences with complex motion activity, modern video coding standards allow variable block sizes to be employed in temporal prediction. A block with complex motion can be partitioned into two equal-sized halves or into four quadrants. In this paper we study the impact of allowing blocks to be partitioned in two unequal subpartitions. Additionally, we allow block partitioning along diagonal edges. These provisions allows encoders to better adapt to the local characteristics of the motion activity in a video sequence. We verified experimentally that the presence of partitioning edges that do not coincide with transform boundaries does not negatively impact the decorrelating strength of the residual transform, so the proposed extended partitioning strategies can be applied regardless of the details of the residual coder. Implementing the proposed extended partitioning modes in an H.264/AVC coder at the macroblock and submacroblock level, we observe that coding efficiency gains are greatest in low-resolution sequences, where moving features in the video sequence tend to be more spatially localized. For CIF and QCIF sequences we achieve a bit rate reduction of about 3–6% over a wide fidelity range.

More and more, multimedia services are being accessed via fixed and mobile networks. These services are typically much more sensitive to packet loss, delay and/or congestion than traditional services. In particular, multimedia data is often time critical and, as a result, network issues are not well tolerated and significantly deteriorate the userpsilas quality of experience (QoE). We therefore propose a QoE optimization platform that is able to mitigate problems that might occur at any location in the delivery path from service provider to customer. More specifically, the distributed architecture supports overlay routing to circumvent erratic parts of the network core. In addition, it comprises proxy components that realize last mile optimization through automatic bandwidth management and the application of processing on multimedia flows. In this paper we introduce a transcoding service for this proxy component which enables the transformation of H.264/AVC video flows to an arbitrary bit rate. Through representative experimental results, we illustrate how this addition enhances the QoE optimization capabilities of the proposed platform by allowing the proxy component to compute more flexible and effective bandwidth distributions.

In this paper, we show that it is possible to efficiently transcode single-layer H.264/AVC bitstreams to SNR-scalable SVC streams with CGS layers. Using requantization error compensation techniques, our architecture is able to restrain the drift that arises due to the absence of a closed prediction loop at the different dependency layers. Implementation results show that transcoding can generate SVC bit-streams with rate-distortion performance approaching that of the rate-distortion optimal encoder within 1 to 2 dB. Gains of more than 2 dB are obtained when compared to open-loop requantization.

Requantization transcoding is a fast technique for video bit-rate reduction. In our previous work, we proposed a hybrid architecture for the requantization of H.264/AVC bitstreams. In this architecture, the B pictures were open-loop transcoded. When B pictures are not used as reference for coding neighboring pictures, this is an acceptable approach, since the requantization errors, originating from the B pictures, can not propagate to other pictures. In this paper, however, we compare different transcoding methods in case the coded video streams are composed of hierarchical B pictures. Here, the necessity for compensating the accumulated requantization error in reference B pictures needs to be investigated. We show that extended compensation techniques result in trans-coded sequences that approach the rate-distortion optimal decoder-encoder cascade within 1 dB. Compensation of intra-coded macroblocks only is proven to be a low-complexity alternative, with only minor concessions in rate-distortion performance.

Due to the increasing heterogeneity in network and terminal capabilities, the delivery of multimedia content has become an important issue today. In order to avoid offering multiple versions of the same media resource, a transparent approach under the form of scalable coding is needed for multimedia delivery systems. This paper proposes a fully format agnostic adaptation engine for scalable bitstreams by relying on an abstract model for these bitstreams. The adaptation technique is implemented by making use of the MPEG-21 Digital Item Adaptation specification. The approach is validated for five different scalable coding formats. Experimental results show that our fully format agnostic adaptation node customizes scalable bitstreams in real time (at a speed of more than 100 Mbit/s).

Cross-fertilization of media and information technologies is causing pressure on traditional broadcasting facilities to re-engineer their production processes and embrace file-based approaches in favor of conventional methods. Traditionally, the production of television and feature film drama is usually supported by a paper information flow and consists of a rather ad hoc production workflow. This paper introduces a data model that thoroughly supports the production processes and workflows of drama programs in a file-based and integrated media architecture. The presented data model is fully compliant with the P/Meta metadata exchange scheme and, as a proof of concept, its validity was verified by the successful development of a centralized drama production application.

The heterogeneity in the contemporary multimedia environments requires a format-agnostic adaptation framework for the consumption of digital video content. Scalable bitstreams can be used in order to satisfy as many circumstances as possible. In this paper, the scalable extension on the H.264/AVC specification is used to obtain the parent bitstreams. The adaptation along the combined scalability axis of the bitstreams is done in a format-independent manner. Therefore, an abstraction layer of the bitstream is needed. In this paper, XML descriptions are used representing the high-level structure of the bitstreams by relying on the MPEG-21 bitstream syntax description language standard. The exploitation of the combined scalability is executed in the XML domain by implementing the adaptation process in a streaming transformation for XML (STX) stylesheet. The algorithm used in the transformation of the XML description is discussed in detail in this paper. From the performance measurements, one can conclude that the STX transformation in the XML domain and the generation of the corresponding adapted bitstream can be realized in real time.

Due to the increasing heterogeneity in the current multimedia landscape, the delivery of multimedia content has become an important issue today. This heterogeneity is not only reflected by a plethora of different usage environments, but also by the presence of multiple (scalable) coding formats. Therefore, format-independent adaptation engines have to be used within a multimedia delivery platform, which are able to adapt the multimedia content according to a certain usage environment, independent of the underlying coding format of the content. By relying on automatically created textual descriptions of the high-level syntax of binary media resources, a format-independent adaptation engine can be built. MPEG-21 generic bitstream syntax schema (gBS schema) is a tool that is part of the MPEG-21 multimedia framework. It enables the use of generic bitstream syntax descriptions (gBSDs), i.e., textual descriptions in XML, to steer the adaptation of a binary media resource, using format-independent adaptation logic. In this paper, we address the design and performance evaluation of a multimedia delivery platform that relies on gBS schema-driven adaptation engines. Our platform is called MuMiVA; it is a fully integrated, extensible platform for multimedia delivery in heterogeneous usage environments, using streaming technologies. To demonstrate the flexibility of our multimedia delivery platform, we discuss the functioning of two different applications (i.e., exploitation of temporal scalability and shot selection) applied to two different coding formats (i.e., MPEG-4 visual and H.264/AVC).

Video source, carrier and client diversification have led the video coding community to develop scalable video codecs supporting efficient decoding at varying resolution, frame rate and quality. Scalable video has several advantages over a nonscalable approach, but a large scale deployment is far from trivial and a lot of open questions remain. To resolve these, we developed a complete video delivery chain for scalable wavelet-based video. This includes a video server, a negotiation framework, a video scaling infrastructure and two scalable video clients, one pure software client and one real-time, hardware accelerated client. This paper describes the complete chain and identifies and quantifies the impact of using scalable video in every link of this chain.

This paper proposes an enhanced error protection scheme using flexible macroblock ordering in H.264/AVC. The algorithm uses a two-phase system. In the first phase, the importance of every macroblock is calculated based on its influence on the current frame and future frames. In the second phase, the macroblocks with the highest impact factor are grouped together in a separate slice group using the flexible macroblock ordering feature of H.264/AVC. By using an unequal error protection scheme, the slice group containing the most important macroblocks can be better protected than the other slice group. The proposed algorithm offers better concealment opportunities than the algorithms which are predefined for flexible macroblock ordering in H.264/AVC.

The heterogeneity in the current and future multimedia environment requires an elegant adaptation framework for the production and consumption of different kinds of multimedia content. Such an architecture is preferably based on the usage of scalable bitstreams and a format-agnostic content adaptation engine. To obtain fully embedded scalable bitstreams, the joint scalable video model (JSVM) has been used in this paper. Hereby, JSVM defines a scalable extension on top of the H.264/AVC specification. This extension will make it possible to create bitstreams that are scalable along the temporal, spatial, and SNR axis. On the other hand, bitstream structure descriptions can be used to realize an elegant and format-agnostic adaptation engine. Such descriptions can be created by making use of the MPEG-21 bitstream syntax description language (BSDL) standard. The latter allows to describe the high-level structure of scalable bitstreams in XML. This paper explains how fully scalable bitstreams can be customized by transforming BSDL-based bitstream structure descriptions. From our performance analysis, one can conclude that the transformation of the XML description, as well as the generation of the adapted bitstream, can be done several times faster than real time

The key concepts in an MPEG-21 architecture are the digital item, representing multimedia data, and Users which interact with it. MPEG-21 introduced digital item processing, which defines ways to add functionality to the item, creating a dynamic and interactive multimedia format. Moreover, an execution environment has been defined, offering basic functionality on any terminal that supports digital item processing. Although the semantics of this functionality has been standardized, there is much room for interpretation, so a digital item author may not be aware of the actual processing. We propose a solution, compliant with the digital item processing specification, to give the content creators full control on the processing of their items, allowing the creation of advanced predictable multimedia systems in an MPEG-21 environment

In the domain of digital video coding, new technologies and solutions are emerging in a fast pace, targeting the needs of the evolving multimedia landscape. One of the questions that arises is how to assess these different video coding technologies in terms of compression efficiency. In this paper, several compression schemes are compared by means of peak signal-to-noise ratio (PSNR) and just noticeable difference (JND). The codecs examined are XviD 0.9.1 (conform to the MPEG-4 Visual Simple Profile), DivX 5.1 (implementing the MPEG-4 Visual Advanced Simple Profile), Windows Media Video 9, MC-EZBC and H.264/AVC AHM 2.0 (version JM 6.1 of the reference software, extended with rate control). The latter plays a key role in this comparison because the H.264/AVC standard can be considered as the de facto benchmark in the field of digital video coding. The obtained results show that H.264/AVC AHM 2.0 outperforms current proprietary and standards-based implementations in almost all cases. Another observation is that the choice of a particular quality metric can influence general statements about the relation between the different codecs.

Within the world of multimedia, the new MPEG-21 standard is currently under development. The purpose of this new standard is to create an open framework for multimedia delivery and consumption. MPEG-21 mastered the multitude of types of content and metadata by standardizing the declaration of digital items in an XML based format. In addition to standardizing, the declaration of digital items MPEG-21 also standardizes digital item processing, which enables the declaration of suggested uses of digital items. The rights expression language and the rights data dictionary parts of MPEG-21 enable the declaration of what rights (permitted interactions) Users are given to digital items. In this paper, we describe how rights checking can be realized in an environment in which interactions with digital items are declared through digital item processing. We demonstrate how rights checking can be done when "critical" digital item base operations are called and how rights context information can be gathered by tracking during the execution of digital item methods

In video coding, certain limitations imposed by the environment, most typically the bit rate, need to be fulfilled. This is achieved by allowing the encoder to reduce the quality in one or several ways, such as the distortion, the resolution and the frame rate. The upcoming scalable video coding mechanisms allow this reduction to take place not exclusively during the encoding step, but at any time. This allows us to reduce the quality in a more personalized way, taking into consideration the preferences of the end user. This paper presents a framework that enables such user dependent quality reductions. We validated this framework by means of a test involving 19 test persons. The results of this mechanism are good, but up to now not sufficiently reliable to use it in commercial applications. At the same time, we still see some room for improvement

In order to efficiently deal with the heterogeneity in the current and future multimedia ecosystem, it is necessary that content can be adapted in a format-agnostic manner. A first step toward a solution, able to fulfill the just mentioned requirement, is to rely on a scalable video codec and to describe the high-level structure of the resulting bitstreams in such a way that every terminal can understand it, in particular by using XML. This paper describes how such descriptions can be generated by making use of the media format independent BintoBSD tool of the MPEG-21 BSDL standard. However, regarding the current status of BSDL, it is impossible to create a description in real time and to keep the generation speed constant over the complete sequence. In this paper, we describe a number of extensions and algorithmic modifications that make it possible to generate a description of a bitstream in real time and at a constant speed. Our approach results in a significant reduction of the original execution times (up to 99% for the H.264/AVC coding format) and in a constant memory usage.

This paper introduces the concept of MPEG-21 Digital Item Processing (DIP), which enables the incorporation of interoperable descriptions of programmability related to the multimedia experience. In the first part of the paper, a motivation for defining DIP is outlined and the different terms and definitions used within DIP are defined and discussed. The second part of the paper gives an overview of the current development status of DIP and discusses the Application Programming Interfaces provided to the Digital Item Method author. This illustrates how DIP can be used to interact between the different parts of MPEG-21. A walkthrough of the DIP process is given enabling the creation of a truly interoperable MPEG-21 DIP peer. Finally, the paper ends with some conclusions and gives some insight on what can be expected for DIP in the future.

Within the world of multimedia, several standards for content encoding and decoding exist. However, with the current capabilities of multimedia devices, only encoding and decoding of multimedia become more and more cumbersome. We explore how interactivity can be added to multimedia content using a new and upcoming standard, ISO/IEC 21000, better known as MPEG-21. The purpose of MPEG-21 is to create an open framework for multimedia delivery and consumption. This framework enables its users to exchange, consume and manipulate a large variety of content in an efficient, transparent and interoperable way. The paper introduces the concept of digital item processing, which allows interactivity and functionality to be included in MPEG-21. The feasibility of the MPEG-21 approach is proven by the implementation of a demonstrator on the J2ME platform. Time and memory performance measurements are outlined to demonstrate the cost of adding extra processing information to multimedia content when using MPEG-21.

H.264/AVC is a video codec developed by the Joint Video Team (JVT); a cooperation between the ITU-T VCEG (Video Coding Experts Group) and ISO/IEC MPEG (Moving Picture Experts Group). This new video coding standard has some new features that allow to get significant improvements in coding efficiency. This improved coding efficiency leads to an overall more complex algorithm which has high demands regarding memory usage and processing power. Complexity, however, is an abstract concept and cannot be measured in a simple manner. In this paper we present a method to obtain an accurate and more in-depth view on the internals of the JVT/AVC decoder. By decoding several bit streams having different encoding parameters, various program characteristics were measured. On these measurements, principal components analysis was performed to get a different view on these measurements. Our results show that the various encoding parameters have a clear impact on the low level behavior of the decoder. Moreover, our methodology allows us to give an explanation for the observed dissimilarities.

The increasing diversity of the characteristics of the terminals and networks that are used to access multimedia content through the internet introduces new challenges for the distribution of multimedia data. Scalable video coding will be one of the elementary solutions in this domain. This type of coding allows to adapt an encoded video sequence to the limitations of the network or the receiving device by means of very basic operations. Algorithms for creating fully scalable video streams, in which multiple types of scalability are offered at the same time, are becoming mature. On the other hand, research on applications that use such bitstreams is only recently emerging. In this paper, we introduce a mathematical model for describing such bitstreams. In addition, we show how we can model applications that use scalable bitstreams by means of definitions that are built on top of this model. In particular, we chose to describe a multicast protocol that is targeted at scalable bitstreams. This way, we will demonstrate that it is possible to define an abstract model for scalable bitstreams, that can be used as a tool for reasoning about such bitstreams and related applications.

Video coding is used under the hood of a lot of multimedia applications, such as video conferencing, digital storage media, television broadcasting, and internet streaming. Recently, new standards-based and proprietary technologies have emerged. An interesting problem is how to evaluate these different video coding solutions in terms of delivered quality. In this paper, a PSNR-based approach is applied in order to compare the coding potential of H.264/AVC AHM 2.0 with the compression efficiency of XviD 0.9.1, DivX 5.05, Windows Media Video 9, and MC-EZBC. Our results show that MPEG-4-based tools, and in particular H.264/AVC, can keep step with proprietary solutions. The rate-distortion performance of MC-EZBC, a wavelet-based video codec, looks very promising too.

Image-scrambling schemes are designed to render the image content unintelligible. Wyner has proposed an elegant one-dimensional (1-D) scrambling scheme without bandwidth expansion, making use of the discrete prolate spheroidal sequences (DPSS). The DPSS are optimal regarding their energy concentration in a given frequency subband. In this paper, we propose the two-dimensional (2-D) extension and application of this algorithm. We discuss new possibilities introduced by the 2-D approach. We also include experimental results.

This paper proposes a new family of bivariate, nonseparable splines, called hex-splines, especially designed for hexagonal lattices. The starting point of the construction is the indicator function of the Voronoi cell, which is used to define in a natural way the first-order hex-spline. Higher order hex-splines are obtained by successive convolutions. A mathematical analysis of this new bivariate spline family is presented. In particular, we derive a closed form for a hex-spline of arbitrary order. We also discuss important properties, such as their Fourier transform and the fact they form a Riesz basis. We also highlight the approximation order. For conventional rectangular lattices, hex-splines revert to classical separable tensor-product B-splines. Finally, some prototypical applications and experimental results demonstrate the usefulness of hex-splines for handling hexagonally sampled data.

In recent years. a large number of new multimedia (MM) formats have been created. It is widely recognized that the life cycle for a MM document consists of three areas. First of all, a document model is needed to model the MM scenario. Secondly, this document model is to be translated into a computer description. Finally, a set of synchronization primitives must be defined in order to be able to present the computer description to the end-user. The contribution of this paper is the definition of an integrated reference model that covers all three areas. For each of the three areas a model is put forward, based on the current state-of-the-art in that area. From these models, a reference model is created consisting of ten rules and a document model. This model is then applied to a number of real-world MM formats (SMIL, QuickTime, RealVideo, Advanced Streaming Format, Shockwave, and MPEG-4). Finally, a comparison is presented showing the results obtained from applying the reference model to the MM formats.

Resampling techniques are commonly required in digital image processing systems. Many times the classical interpolation functions are used, i.e., nearest-neighbour interpolation and bilinear interpolation, which are prone to the introduction of undesirable artifacts due to aliasing such as moire patterns. This paper presents a novel approach which minimizes the loss of information, in a least-squares sense, while resampling between orthogonal and hexagonal lattices. Making use of an extension of 2D splines to hexagonal lattices, the proper reconstruction function is derived. Experimental results for a printing application demonstrate the feasibility of the proposed method and are compared against the classical techniques.

We describe the validation process of a functional metadata document encapsulated in an MPEG-21 digital item. The validation starts with an MPEG-21 digital item containing the functional metadata and ends with a valid functional metadata document or with a list of error codes. Since the W3C schema language does not suffice to validate all relationships within a functional metadata document, a set of extra rules is defined. These rules are implemented through the use of transformations on functional metadata documents. These transformations are implemented using XSLT.

In the digital prepress workflow, images are represented in the CMYK colour space. Lossy image compression alleviates the need for high storage and bandwidth capacities, resulting from the high spatial and tonal resolution. After the image has been printed on paper, the introduced visual quality loss should not be noticeable to a human observer. Since visual image quality depends on the compression algorithm both quantitatively and qualitatively, and since no visual image quality models incorporating the end‐to‐end image reproduction process are satisfactory, an experimental comparison is the only viable way to quantify subjective image quality. This paper presents the results from an intensive psychovisual study based on a two‐alternative forced‐choice approach involving 164 people, with expert and non‐expert observers distinguished. The primary goal is to evaluate two previously published adaptations of JPEG to CMYK images, and to determine a visually lossless compression ratio threshold for typical printing applications. The improvements are based on tonal decorrelation and overlapping block transforms. Results on three typical prepress test images indicate that the proposed adaptations are useful and that for the investigated printing configuration, compression ratios up to 20 can be used safely.

In quantitative coronary angiography (QCA), quantification parameters of the coronary arteries are determined using two dimensional (2D) projections of the three dimensional (3D) vessel structures. Unfortunately, the results of this quantification depend on the viewing angles corresponding with the used projections. To deal with this viewpoint dependency, multiple computer systems have been developed to determine the optimal angiographic viewing angles, the angles corresponding with the viewpoints perpendicular to the vessel segments of interest, which are the best possible viewpoints to perform QCA. We developed a system that determines the optimal viewpoints and QCA parameters without creating a reconstruction. Hence the proposed system is very simple and fast. The variation and deviation on results obtained using images of static phantoms are much smaller the inaccuracies due to the movements of the arteries.

In this work, we describe a method that uses the linear phase acquired during the readout period due to chemical shift to generate individual magnetic resonance (MR) images of chemically shifted species. The method utilizes sets of Fourier (or k-space) data acquired with different directions of the readout gradient and a postprocessing algorithm to generate chemical shift images. The methodology is developed for both Cartesian data acquisition and for radial data acquisition. The method is presented here for two chemically shifted species but it can be extended to more species. In this work, we present the theory, show the results in phantoms and in human images, and discuss the artifacts and signal-to-noise ratio of the images obtained with the technique.

A minimum-norm least-squares image-reconstruction method for the reconstruction of magnetic resonance images from non-Cartesian sampled data is proposed. The method is based on a general formalism for continuous-to-discrete mapping and pseudoinverse calculation. It does not involve any regridding or interpolation of the data and therefore the methodology differs fundamentally from existing regridding-based methods. Moreover, the method uses a continuous representation of objects in the image domain instead of a discretized representation. Simulations and experiments show the possibilities of the method in both radial and spiral imaging. Simulations revealed that minimum-norm least-squares image reconstruction can result in a drastic decrease of artifacts compared with regridding-based reconstruction. Besides, both in vivo and phantom experiments showed that minimum-norm least-squares image reconstruction leads to contrast improvement and increased signal-to-noise ratio compared with image reconstruction based on regridding. As an appendix, an analytical calculation of the raw data corresponding to the well-known Shepp and Logan software head phantom is presented.

We present two new quantitative methods to determine motion intervals in projection reconstruction (PR) magnetic resonance (MR) imaging. Both methods do not use any a priori information concerning the motion and can be implemented easily. In the first section of this paper, the theoretical concept that formed the basis for the development of these methods, is described. It is shown that the phase difference between the complex means of two consecutive 1D PR signals equals zero when no motion occurs. When motion does occur, it becomes large. Similar conclusions can be drawn when the root mean square deviation between two consecutive projections is considered: it remains very small as long as no motion occurs, while it has a large value when rotational motion or translational motion with high-frequency components occur. These conclusions are used to determine the motion intervals in PR data. The motion-detection methods prove to be able to detect motion intervals with an accuracy of one repetition time.